






from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray







def       lowercase__	(					A_:						NDArray[floataa]  ,      A_:						NDArray[floataa]  ,      A_:						list[int]  ,      A_:						int  ,      )	->				list[float]:







       """simple docstring"""






       __UpperCAmelCase				, __UpperCAmelCase     				=coefficient_matrix.shape
       __UpperCAmelCase				, __UpperCAmelCase     				=constant_matrix.shape

       if rowsa != colsa:
              __UpperCAmelCase     				=F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
              raise ValueError(A_		)

       if colsa != 1:
              __UpperCAmelCase     				=F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
              raise ValueError(A_		)

       if rowsa != rowsa:
              __UpperCAmelCase     				=(
                  """Coefficient and constant matrices dimensions must be nxn and nx1 but """
                  F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
              )
              raise ValueError(A_		)

       if len(A_		) != rowsa:
              __UpperCAmelCase     				=(
                  """Number of initial values must be equal to number of rows in coefficient """
                  F'''matrix but received {len(A_		)} and {rowsa}'''
              )
              raise ValueError(A_		)

       if iterations <= 0:
              raise ValueError("""Iterations must be at least 1"""		)

       __UpperCAmelCase     				=np.concatenate(
           (coefficient_matrix, constant_matrix)  ,      axis=1		)

       __UpperCAmelCase				, __UpperCAmelCase     				=table.shape

       strictly_diagonally_dominant(A_		)

       # Iterates the whole matrix for given number of times
       for _ in range(A_		):
              __UpperCAmelCase     				=[]
              for row in range(A_		):
                     __UpperCAmelCase     				=0
                     for col in range(A_		):
                            if col == row:
                                   __UpperCAmelCase     				=table[row][col]
                            elif col == cols - 1:
                                   __UpperCAmelCase     				=table[row][col]
                            else:
                                   temp += (-1) * table[row][col] * init_val[col]
                     __UpperCAmelCase     				=(temp + val) / denom
                     new_val.append(A_		)
              __UpperCAmelCase     				=new_val

       return [float(A_		) for i in new_val]







def       lowercase__	(					A_:						NDArray[floataa]		)	->				bool:







       """simple docstring"""






       __UpperCAmelCase				, __UpperCAmelCase     				=table.shape

       __UpperCAmelCase     				=True

       for i in range(0  ,      A_		):
              __UpperCAmelCase     				=0
              for j in range(0  ,      cols - 1		):
                     if i == j:
                            continue
                     else:
                            total += table[i][j]

              if table[i][i] <= total:
                     raise ValueError("""Coefficient matrix is not strictly diagonally dominant"""		)

       return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
   import doctest

   doctest.testmod()





'''simple docstring'''




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCAmelCase					={
    "configuration_efficientnet": [
        "EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientNetConfig",
        "EfficientNetOnnxConfig",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=["EfficientNetImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=[
      "EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "EfficientNetForImageClassification",
      "EfficientNetModel",
      "EfficientNetPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_efficientnet import (
      EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
      EfficientNetConfig,
      EfficientNetOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_efficientnet import EfficientNetImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_efficientnet import (
        EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        EfficientNetForImageClassification,
        EfficientNetModel,
        EfficientNetPreTrainedModel,
    )

else:
  import sys

  __UpperCAmelCase					=_LazyModule(__name__, globals()["__file__"], _import_structure)
